System and method for identifying target regions prior to organs at risk segmentation

ABSTRACT

A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MIP image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MIP image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

BACKGROUND

Automated organ at risk (OAR) segmentation has been a popular researchtopic since manual segmentation is time consuming andoperator-dependent. Recent developments in deep learning have reducedthe amount of time for segmentation tasks to the order of milliseconds.

However, due to limits of graphics processing unit (GPU) memory, it isoften impractical to feed a large amount of three dimensional (3D)volume data with original sizing into a segmentation network. To solvethis problem, some techniques utilize cropping, sliding windows, ordownsampling as preprocessing steps. Downsampling might remove highfrequency information of the input image, and as a result reduces thefollowing segmentation accuracy. Manual cropping introduces humaninteraction and is inefficient. Sliding window ensemble methods mayimprove segmentation accuracy slightly, but is more time-consuming inthe inference stage.

This present disclosure efficiently detects the bounding box of the headand neck, narrows the input to the region of interest, and saves GPUmemory resources while maintaining image detail.

SUMMARY

According to an aspect of the disclosure, a method for generating athree dimensional (3D) bounding box of a region of interest (ROI) of apatient, includes receiving, by a device, a two dimensional (2D) maximumintensity projection (MIP) image that is an axial view of the patient;receiving, by the device, a 2D MIP image that is a sagittal view of thepatient; detecting, by the device, a first 2D bounding box of the ROI ofthe patient using the 2D MIP image that is the axial view of thepatient; detecting, by the device, a second 2D bounding box of the ROIof the patient using the 2D MIP image that is the sagittal view of thepatient; receiving, by the device, a 3D MIP image of the patient;generating, by the device, the 3D bounding box of the ROI of the patientusing the first 2D bounding box, the second 2D bounding box, and the 3DMIP image of the patient; and providing, by the device, the 3D MIP imageincluding the 3D bounding box of the ROI of the patient to permit organat risk (OAR) segmentation using the 3D MIP image including the 3Dbounding box of the ROI.

According to an aspect of the disclosure, a device for generating athree dimensional (3D) bounding box of a region of interest (ROI) of apatient comprises at least one memory configured to store program code;and at least one processor configured to read the program code andoperate as instructed by the program code, the program code including:receiving code that is configured to cause the at least one processor toreceive a two dimensional (2D) maximum intensity projection (MIP) imagethat is an axial view of the patient, receive a 2D MIP image that is asagittal view of the patient, and receive a 3D MIP image of the patient;detecting code that is configured to cause the at least one processor todetect a first 2D bounding box of the ROI of the patient using the 2DMIP image that is the axial view of the patient, and detect a second 2Dbounding box of the ROI of the patient using the 2D MIP image that isthe sagittal view of the patient; generating code that is configured tocause the at least one processor to generate the 3D bounding box of theROI of the patient using the first 2D bounding box, the second 2Dbounding box, and the 3D MIP image of the patient; and providing codethat is configured to cause the at least one processor to provide the 3DMIP image including the 3D bounding box of the ROI of the patient topermit organ at risk (OAR) segmentation using the 3D MIP image includingthe 3D bounding box of the ROI.

According to an aspect of the disclosure, a non-transitorycomputer-readable medium stores instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors of a device, cause the one or more processors to: receive atwo dimensional (2D) maximum intensity projection (MIP) image that is anaxial view of the patient; receive a 2D MIP image that is a sagittalview of the patient; detect a first 2D bounding box of the ROI of thepatient using the 2D MIP image that is the axial view of the patient;detect a second 2D bounding box of the ROI of the patient using the 2DMIP image that is the sagittal view of the patient; receive a 3D MIPimage of the patient; generate the 3D bounding box of the ROI of thepatient using the first 2D bounding box, the second 2D bounding box, andthe 3D MIP image of the patient; and provide the 3D MIP image includingthe 3D bounding box of the ROI of the patient to permit organ at risk(OAR) segmentation using the 3D MIP image including the 3D bounding boxof the ROI.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an example process for generating a threedimensional (3D) bounding box of a region of interest (ROI) of a patientusing two dimensional (2D) image data.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented; and

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

DETAILED DESCRIPTION

Deep learning (DL) based segmentation networks have been widely used forthe border delineation of organs at risk (OAR) and targeted tumors intreatment planning of radiotherapy. However, 3D based segmentationnetworks often suffer from limited GPU memory resources if the inputimage volumes are of original size (e.g., include a file size above aparticular predetermined threshold).

The present disclosure addresses this problem in 3D OAR segmentation byautomatically detecting a bounding box of a body part of interest priorto the DL based segmentation step. The OAR segmentation is performedwithin the detected bounding box in order to reduce resourceconsumption. To reduce the memory requirement of the bounding boxdetection network, the an embodiment of the present disclosure detectstwo dimensional (2D) bounding boxes using 2D maximum intensityprojection images derived from the saggital and axial views rather thanusing the original 3D image. The 3D bounding box is then constructedfrom the 2D bounding boxes in the 2D maximum intensity projection (MIP)images.

An embodiment of the present disclosure automatically detects regions ofinterest (ROIs) from the human body (e.g., the head and neck area ofcomputed tomography (CT) images scanned for patients with head and neckcancer).

An embodiment of the present disclosure performs detection using 2D MIPprojection images instead of 3D images, thus conserving GPU memory andprocessing resources as compared to using 3D images for bounding boxdetection. After identifying the bounding box of the head and neck area,an embodiment of the present disclosure narrows the input volume to thehead and neck area of interest. Therefore, an embodiment of the presentdisclosure addresses the GPU memory limitation problem in the followingsegmentation stage as well.

As a particular example, the present disclosure provides an efficientway of ROI detection (e.g., head and neck) in CT images from patientswith head and neck cancer. For example, an embodiment detects thebounding box of the head and neck using 2D projection images, and thussaves memory in the bounding box detection stage as compared toperforming detection using 3D image data directly. This is beneficialfor deep learning based OAR segmentation if the input volume of thesegmentation is the detected head and neck rather than the whole volumeincluding, for example, data associated with a bed.

Detecting a 3D bounding box requires more GPU memory consumption ascompared to detecting a 2D bounding box. Accordingly, an embodiment ofthe present disclosure detects the bounding boxes using 2D MIP images.

An embodiment of the disclosure may utilize a fast region basedconvolutional neural network (RCNN), a faster RCNN, a mask RCNN, or thelike, for 2D deep learning based bounding box detection.

For instance, in a faster RCNN architecture, an input image is sent to aconvolutional network to extract a feature map. The feature map is thensent to a region proposal network to predict candidate regions. The sizeof the proposed regions are further reshaped and sent to a classifierwhich classifies the information in the proposed regions, and predictsthe actual shape of the proposed region.

In the present disclosure, an embodiment includes two classes in eachproposed region (i.e., the axial and the sagittal MIP). Regarding theregion size, an embodiment may predict four coordinates of minimum x,minimum y, maximum x and maximum y for each proposed region.

According to an embodiment, the 3D bounding box might include sixcoordinates, such as minimum x, maximum x, minimum y, maximum y, minimumz, and maximum z. The first four coordinates (i.e., minimum x, maximumx, minimum y, and maximum y) are determined based on the coordinates inthe axial MIP images. Further, the remaining coordinates (i.e., theminimum z and maximum z) are determined based on the minimum x andmaximum x in the sagittal image.

The present disclosure provides, among other things, the followingtechnical benefits: 1) reduced GPU memory footprint that permits ROIrecognition possible and viable; and 2) improved workflow andefficiency.

FIG. 1 is a flow chart of an example process 100 for generating a threedimensional (3D) bounding box of a region of interest (ROI) of apatient. In some implementations, one or more process blocks of FIG. 1may be performed by platform 220. In some implementations, one or moreprocess blocks of FIG. 1 may be performed by another device or a groupof devices separate from or including platform 220, such as user device210.

As shown in FIG. 1, process 100 may include receiving, by a device, atwo dimensional (2D) maximum intensity projection (MIP) image that is anaxial view of the patient (block 105).

For example, platform 220 may receive a 2D MIP image that is an axialview of a patient. The platform 220 may receive the 2D MIP image fromanother device, based on an input from an operator, from a cloud storagedevice, or the like.

The patient may refer to a person, an animal, a phantom, and object, orthe like, for which an ROI is to be detected. The ROI may refer to aregion for which organ at risk (OAR) processing is to be performed. Asan example, for a patient with head and neck cancer, the ROI may includea head and neck of the patient. It should be understood that the ROI mayvary based on the patient. The axial view of the patient may refer to aplan view of the patient.

As further shown in FIG. 1, process 100 may include receiving, by thedevice, a 2D MIP image that is a sagittal view of the patient (block110).

As further shown in FIG. 1, process 100 may include detecting, by thedevice, a first 2D bounding box of the ROI of the patient using the 2DMIP image that is the axial view of the patient (block 115).

As further shown in FIG. 1, process 100 may include detecting, by thedevice, a second 2D bounding box of the ROI of the patient using the 2DMIP image that is the sagittal view of the patient (block 120).

As further shown in FIG. 1, process 100 may include receiving, by thedevice, a 3D MIP image of the patient (block 125).

As further shown in FIG. 1, process 100 may include identifying whetherall data points of the first 2D bounding box and the second 2D boundingbox have been obtained (block 130).

As further shown in FIG. 1, if all of the data points have not beenobtained (block 130—NO), then process 100 may include obtainingadditional data points.

As further shown in FIG. 1, if all of the data points have been obtained(block 130—YES), then process 100 may include generating, by the device,the 3D bounding box of the ROI of the patient using the first 2Dbounding box, the second 2D bounding box, and the 3D MIP image of thepatient (block 135).

As further shown in FIG. 1, process 100 may include providing, by thedevice, the 3D MIP image including the 3D bounding box of the ROI of thepatient to permit organ at risk (OAR) segmentation using the 3D MIPimage including the 3D bounding box of the ROI (140).

Although FIG. 1 shows example blocks of process 100, in someimplementations, process 100 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 1. Additionally, or alternatively, two or more of theblocks of process 100 may be performed in parallel.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a user device 210, a platform 220, and anetwork 230. Devices of environment 200 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith platform 220. For example, user device 210 may include a computingdevice (e.g., a desktop computer, a laptop computer, a tablet computer,a handheld computer, a smart speaker, a server, etc.), a mobile phone(e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g.,a pair of smart glasses or a smart watch), or a similar device. In someimplementations, user device 210 may receive information from and/ortransmit information to platform 220.

Platform 220 includes one or more devices capable of generating a threedimensional (3D) bounding box of a region of interest (ROI) of apatient, as described elsewhere herein. In some implementations,platform 220 may include a cloud server or a group of cloud servers. Insome implementations, platform 220 may be designed to be modular suchthat certain software components may be swapped in or out depending on aparticular need. As such, platform 220 may be easily and/or quicklyreconfigured for different uses.

In some implementations, as shown, platform 220 may be hosted in cloudcomputing environment 222. Notably, while implementations describedherein describe platform 220 as being hosted in cloud computingenvironment 222, in some implementations, platform 220 is not becloud-based (i.e., may be implemented outside of a cloud computingenvironment) or may be partially cloud-based.

Cloud computing environment 222 includes an environment that hostsplatform 220. Cloud computing environment 222 may provide computation,software, data access, storage, etc. services that do not requireend-user (e.g., user device 210) knowledge of a physical location andconfiguration of system(s) and/or device(s) that hosts platform 220. Asshown, cloud computing environment 222 may include a group of computingresources 224 (referred to collectively as “computing resources 224” andindividually as “computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 224 may host platform 220. The cloud resources may includecompute instances executing in computing resource 224, storage devicesprovided in computing resource 224, data transfer devices provided bycomputing resource 224, etc. In some implementations, computing resource224 may communicate with other computing resources 224 via wiredconnections, wireless connections, or a combination of wired andwireless connections.

As further shown in FIG. 2, computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by user device 210 and/or sensor device 220.Application 224-1 may eliminate a need to install and execute thesoftware applications on user device 210. For example, application 224-1may include software associated with platform 220 and/or any othersoftware capable of being provided via cloud computing environment 222.In some implementations, one application 224-1 may send/receiveinformation to/from one or more other applications 224-1, via virtualmachine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., user device 210), and may manage infrastructure of cloudcomputing environment 222, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210 and/or platform 220. As shown in FIG.3, device 300 may include a bus 310, a processor 320, a memory 330, astorage component 340, an input component 350, an output component 360,and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes in response to processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method for generating a three dimensional (3D)bounding box of a region of interest (ROI) of a patient, comprising:receiving, by a device, a two dimensional (2D) maximum intensityprojection (MIP) image that is an axial view of the patient; receiving,by the device, a 2D MIP image that is a sagittal view of the patient;detecting, by the device, a first 2D bounding box of the ROI of thepatient using the 2D MIP image that is the axial view of the patient;detecting, by the device, a second 2D bounding box of the ROI of thepatient using the 2D MIP image that is the sagittal view of the patient;receiving, by the device, a 3D MIP image of the patient; generating, bythe device, the 3D bounding box of the ROI of the patient using thefirst 2D bounding box, the second 2D bounding box, and the 3D MIP imageof the patient; and providing, by the device, the 3D MIP image includingthe 3D bounding box of the ROI of the patient to permit organ at risk(OAR) segmentation using the 3D MIP image including the 3D bounding boxof the ROI.
 2. The method of claim 1, further comprising: removing, bythe device, data of the 3D MIP image that is not within the 3D boundingbox; and providing, by the device, the 3D MIP image from which the datahas been removed.
 3. The method of claim 1, further comprising:inputting, by the device, the 2D MIP image that is the axial view of thepatient, and the 2D MIP image that is the sagittal view of the patientinto a region based convolutional neural network (RCNN); and detecting,by the device, the first 2D bounding box and the second 2D bounding boxbased on an output of the RCNN.
 4. The method of claim 1, furthercomprising: identifying, by the device, a first minimum horizontalvalue, a first maximum horizontal value, a first minimum vertical value,and a first maximum vertical value of the first 2D bounding box of theROI.
 5. The method of claim 4, further comprising: identifying, by thedevice, a second minimum horizontal value and a second maximumhorizontal value of the second 2D bounding box of the ROI.
 6. The methodof claim 5, further comprising: generating, by the device, the 3Dbounding box of the ROI using the first minimum horizontal value, thefirst maximum horizontal value, the first minimum vertical value, andthe first maximum vertical value of the first 2D bounding box of theROI, and using the second minimum horizontal value and the secondmaximum horizontal value of the second 2D bounding box of the ROI. 7.The method of claim 1, wherein the ROI is a head of the patient.
 8. Adevice for generating a three dimensional (3D) bounding box of a regionof interest (ROI) of a patient, comprising: at least one memoryconfigured to store program code; at least one processor configured toread the program code and operate as instructed by the program code, theprogram code including: receiving code that is configured to cause theat least one processor to receive a two dimensional (2D) maximumintensity projection (MIP) image that is an axial view of the patient,receive a 2D MIP image that is a sagittal view of the patient, andreceive a 3D MIP image of the patient; detecting code that is configuredto cause the at least one processor to detect a first 2D bounding box ofthe ROI of the patient using the 2D MIP image that is the axial view ofthe patient, and detect a second 2D bounding box of the ROI of thepatient using the 2D MIP image that is the sagittal view of the patient;generating code that is configured to cause the at least one processorto generate the 3D bounding box of the ROI of the patient using thefirst 2D bounding box, the second 2D bounding box, and the 3D MIP imageof the patient; and providing code that is configured to cause the atleast one processor to provide the 3D MIP image including the 3Dbounding box of the ROI of the patient to permit organ at risk (OAR)segmentation using the 3D MIP image including the 3D bounding box of theROI.
 9. The device of claim 8, further comprising: removing code that isconfigured to cause the at least one processor to remove data of the 3DMIP image that is not within the 3D bounding box, and wherein theproviding code is further configured to cause the at least one processorto provide the 3D MIP image from which the data has been removed. 10.The device of claim 8, further comprising: inputting code that isconfigured to cause the at least one processor to input the 2D MIP imagethat is the axial view of the patient, and the 2D MIP image that is thesagittal view of the patient into a region based convolutional neuralnetwork (RCNN), and wherein the detecting code is further configured tocause the at least one processor to detect the first 2D bounding box andthe second 2D bounding box based on an output of the RCNN.
 11. Thedevice of claim 8, further comprising: identifying code that is furtherconfigured to cause the at least one processor to identify a firstminimum horizontal value, a first maximum horizontal value, a firstminimum vertical value, and a first maximum vertical value of the first2D bounding box of the ROI.
 12. The device of claim 11, wherein theidentifying code is further configured to cause the at least oneprocessor to identify a second minimum horizontal value and a secondmaximum horizontal value of the second 2D bounding box of the ROI. 13.The device of claim 12, wherein the generating code is furtherconfigured to cause the at least one processor to generate the 3Dbounding box of the ROI using the first minimum horizontal value, thefirst maximum horizontal value, the first minimum vertical value, andthe first maximum vertical value of the first 2D bounding box of theROI, and using the second minimum horizontal value and the secondmaximum horizontal value of the second 2D bounding box of the ROI. 14.The device of claim 8, wherein the ROI is a head of the patient.
 15. Anon-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors of a device for generating a three dimensional(3D) bounding box of a region of interest (ROI) of a patient, cause theone or more processors to: receive a two dimensional (2D) maximumintensity projection (MIP) image that is an axial view of the patient;receive a 2D MIP image that is a sagittal view of the patient; detect afirst 2D bounding box of the ROI of the patient using the 2D MIP imagethat is the axial view of the patient; detect a second 2D bounding boxof the ROI of the patient using the 2D MIP image that is the sagittalview of the patient; receive a 3D MIP image of the patient; generate the3D bounding box of the ROI of the patient using the first 2D boundingbox, the second 2D bounding box, and the 3D MIP image of the patient;and provide the 3D MIP image including the 3D bounding box of the ROI ofthe patient to permit organ at risk (OAR) segmentation using the 3D MIPimage including the 3D bounding box of the ROI.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions cause the one or more processors to: remove data of the 3DMIP image that is not within the 3D bounding box; and provide the 3D MIPimage from which the data has been removed.
 17. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions cause the one or more processors to: input the 2D MIP imagethat is the axial view of the patient, and the 2D MIP image that is thesagittal view of the patient into a region based convolutional neuralnetwork (RCNN); and detect the first 2D bounding box and the second 2Dbounding box based on an output of the RCNN.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions cause the one or more processors to: Identify a firstminimum horizontal value, a first maximum horizontal value, a firstminimum vertical value, and a first maximum vertical value of the first2D bounding box of the ROI.
 19. The non-transitory computer-readablemedium of claim 18, wherein the one or more instructions cause the oneor more processors to: identify a second minimum horizontal value and asecond maximum horizontal value of the second 2D bounding box of theROI.
 20. The non-transitory computer-readable medium of claim 19,wherein the one or more instructions cause the one or more processorsto: generate the 3D bounding box of the ROI using the first minimumhorizontal value, the first maximum horizontal value, the first minimumvertical value, and the first maximum vertical value of the first 2Dbounding box of the ROI, and using the second minimum horizontal valueand the second maximum horizontal value of the second 2D bounding box ofthe ROI.